Detail publikace

Optimization of the Wavelet Wiener Filtering for ECG Signals

Originální název

Optimization of the Wavelet Wiener Filtering for ECG Signals

Anglický název

Optimization of the Wavelet Wiener Filtering for ECG Signals

Jazyk

en

Originální abstrakt

This paper deals with the methods of ECG signals denoising via wavelet Wiener filtering. We have studied the influence of the input parameters setting on filtered signals in a consideration of achieved signal to noise ratio (SNR). The Wiener filtering is used in the shift invariant dyadic discrete time wavelet domain for suppression of a parasite electromyographic (EMG) signal. To improve the filtering performance we used the adaptive adjustment of the method parameters, according to the level of the input noise. We are able to increase the average SNR of the whole tested database almost about 10 dB. The proposed algorithm provides better results, than a classic wavelet Wiener filtering method. The algorithm was tested on signals from the standard multilead CSE database.

Anglický abstrakt

This paper deals with the methods of ECG signals denoising via wavelet Wiener filtering. We have studied the influence of the input parameters setting on filtered signals in a consideration of achieved signal to noise ratio (SNR). The Wiener filtering is used in the shift invariant dyadic discrete time wavelet domain for suppression of a parasite electromyographic (EMG) signal. To improve the filtering performance we used the adaptive adjustment of the method parameters, according to the level of the input noise. We are able to increase the average SNR of the whole tested database almost about 10 dB. The proposed algorithm provides better results, than a classic wavelet Wiener filtering method. The algorithm was tested on signals from the standard multilead CSE database.

BibTex


@inproceedings{BUT73846,
  author="Lukáš {Smital} and Martin {Vítek} and Jiří {Kozumplík}",
  title="Optimization of the Wavelet Wiener Filtering for ECG Signals",
  annote="This paper deals with the methods of ECG signals denoising via wavelet Wiener filtering. We have studied the influence of the input parameters setting on filtered signals in a consideration of achieved signal to noise ratio (SNR). The Wiener filtering is used in the shift invariant dyadic discrete time wavelet domain for suppression of a parasite electromyographic (EMG) signal. To improve the filtering performance we used the adaptive adjustment of the method parameters, according to the level of the input noise. We are able to increase the average SNR of the whole tested database almost about 10 dB. The proposed algorithm provides better results, than a classic wavelet Wiener filtering method. The algorithm was tested on signals from the standard multilead CSE database.",
  address="ACM New York, NY, USA",
  booktitle="ACM digital library: 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies",
  chapter="73846",
  howpublished="online",
  institution="ACM New York, NY, USA",
  year="2011",
  month="october",
  pages="1--5",
  publisher="ACM New York, NY, USA",
  type="conference paper"
}